An improved detection method based on morphology and profile analysis for bridge extraction from Lidar

摘要

Extraction of bridges from light detection and ranging (Lidar) images is a difficult problem with low detection accuracy and detection efficiency because of strong dependence on bridge shapes, the influence of vegetation and the large amount of data. This paper proposes an improved method based on morphology and profile analysis to extract bridges with removing background objects such as vegetation. To remove vegetation effectively, a new morphological three dimensional (3D) discrete points operator is applied which the gridding method is utilized to obtain the neighborhoods of the discrete points. Subsequently, union-find sets is incorporated with the profile analysis method to optimize the process of generating the minimum spanning tree (MST) and determining connected domains, which are used to remove large objects and leave the bridges behind. By combining the optimized profile analysis method with the topological characteristics of bridges, bridges are extracted without dependence on their shapes. Finally, in order to improve the computation efficiency of the Lidar data, the OpenMP is employed. Experimental results show that the proposed method can extract the bridges from Lidar data effectively.